Detecting meal ingestion or missed bolus

Abstract

A computer-implemented method of detecting a missed bolus comprises: receiving blood glucose values and bolus information regarding a person with diabetes, the blood glucose values and the bolus information relating to a period of time; classifying, based on the blood glucose values and the bolus information, each of multiple time periods within the period of time regarding whether the time period is associated with ingestion of a meal by the person; performing regression analysis on the classified multiple time periods to identify a first time period of the classified multiple time periods as corresponding to a beginning of the meal, wherein the bolus information indicates no bolus associated with the identified first time period; and associating, based on the regression analysis, a missed bolus event with the meal.

What is claimed is: 
     
         1 . A computer-implemented method of detecting a missed bolus, the method comprising:
 receiving blood glucose values and bolus information regarding a person with diabetes, the blood glucose values and the bolus information relating to a period of time;   classifying, based on the blood glucose values and the bolus information, each of multiple time periods within the period of time regarding whether the time period is associated with ingestion of a meal by the person;   performing regression analysis on the classified multiple time periods to identify a first time period of the classified multiple time periods as corresponding to a beginning of the meal, wherein the bolus information indicates no bolus associated with the identified first time period; and   associating, based on the regression analysis, a missed bolus event with the meal.   
     
     
         2 . The computer-implemented method of  claim 1 , further comprising performing at least one action based on the missed bolus event being associated with the meal. 
     
     
         3 . The computer-implemented method of  claim 2 , wherein performing the at least one action comprises waiting a predetermined time after the beginning of the meal before determining that the bolus was missed. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein classifying the multiple time periods comprises providing the blood glucose values and the bolus information to a first machine-learning set including at least one classifier, and wherein performing the regression analysis comprises providing at least an output of the first machine-learning set to a second machine-learning set including at least one regressor. 
     
     
         5 . The computer-implemented method of  claim 4 , further comprising providing the at least one classifier with an aggregation of at least one of the blood glucose values or the bolus information. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein receiving the bolus information comprises receiving data generated by a pen cap based on the pen cap detecting at least one of a removal of the pen cap from an insulin pen, or a replacement of the pen cap on the insulin pen. 
     
     
         7 . The computer-implemented method of  claim 1 , further comprising generating, based on the blood glucose values and the bolus information, a meal probability density for the person. 
     
     
         8 . The computer-implemented method of  claim 7 , further comprising taking into account the meal probability density before associating the missed bolus event with the meal. 
     
     
         9 . The computer-implemented method of  claim 8 , further comprising taking into account the meal probability density before issuing an alert regarding the person based on the missed bolus event being associated with the meal, wherein a relatively lower threshold for the alert is used when a current meal probability is relatively higher, wherein a relatively higher threshold for the alert is used when a current meal probability is relatively lower. 
     
     
         10 . The computer-implemented method of  claim 9 , wherein taking into account the meal probability density comprises changing a threshold for a feature in a machine-learning classifier based on a kernel smoothed density estimate in the meal probability density. 
     
     
         11 . The computer-implemented method of  claim 9 , wherein taking into account the meal probability density comprises reducing a threshold for associating the missed bolus event with the meal. 
     
     
         12 . The computer-implemented method of  claim 7 , wherein generating the meal probability density comprises using a bolus density as a proxy for meal occurrence likelihood. 
     
     
         13 . The computer-implemented method of  claim 12 , further comprising generating the bolus density by aggregating bolus events for the person that are included in the bolus information. 
     
     
         14 . The computer-implemented method of  claim 13 , wherein the bolus events are distributed within the period of time, and wherein aggregating the bolus events comprises:
 wrapping the bolus events over a time interval shorter than the period of time so that the bolus events are distributed within the time interval;   generating, based on the bolus events wrapped over the time interval, a continuous distribution of likelihood over the time interval; and   replicating the continuous distribution of likelihood at least once within the period of time.   
     
     
         15 . The computer-implemented method of  claim 14 , wherein generating the continuous distribution of likelihood comprises smoothing the bolus events wrapped over the time interval. 
     
     
         16 . The computer-implemented method of  claim 15 , wherein smoothing the bolus events comprises performing a kernel smoothed density estimate. 
     
     
         17 . The computer-implemented method of  claim 14 , wherein generating the continuous distribution of likelihood comprises filtering the bolus events wrapped over the time interval. 
     
     
         18 . The computer-implemented method of  claim 1 , further comprising generating, based on the blood glucose values and the bolus information, a bolus density for the person, wherein generating the bolus density comprises using a meal probability density for the person. 
     
     
         19 . The computer-implemented method of  claim 1 , wherein the bolus information includes bolus events that are delta functions, the method further comprising broadening, before classifying the multiple time periods, each of the bolus events to have a finite time duration. 
     
     
         20 . The computer-implemented method of  claim 19 , wherein no bolus being associated with the identified first time period comprises that the bolus information includes no bolus separated from the identified first time period by at most a predefined time, and wherein the finite time duration is a multiple of the predefined time. 
     
     
         21 . The computer-implemented method of  claim 19 , further comprising aggregating, before classifying the multiple time periods, the bolus events in multiple overlapping time intervals. 
     
     
         22 . An insulin treatment device comprising:
 an input device to receive blood glucose values and bolus information regarding a person with diabetes, the blood glucose values and the bolus information relating to a period of time;   a classifier implemented by at least one processor, the classifier to classify, based on the blood glucose values and the bolus information, each of multiple time periods within the period of time regarding whether the time period is associated with ingestion of a meal by the person; and   a regressor implemented by at least one processor, the regressor to perform regression analysis on the classified multiple time periods to identify a first time period of the classified multiple time periods as corresponding to a beginning of the meal, wherein the bolus information indicates no bolus associated with the identified first time period;   wherein the insulin treatment device associates, based on the regression analysis, a missed bolus event with the meal.   
     
     
         23 . A computer program product stored in a non-transitory storage medium, the computer program product including instructions that when executed by at least one processor cause the at least one processor to perform operations comprising:
 receiving blood glucose values and bolus information regarding a person with diabetes, the blood glucose values and the bolus information relating to a period of time;   classifying, based on the blood glucose values and the bolus information, each of multiple time periods within the period of time regarding whether the time period is associated with ingestion of a meal by the person;   performing regression analysis on the classified multiple time periods to identify a first time period of the classified multiple time periods as corresponding to a beginning of the meal, wherein the bolus information indicates no bolus associated with the identified first time period; and   associating, based on the regression analysis, a missed bolus event with the meal.